# OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios

## TL;DR

OREOS introduces a deep learning-based method for oriented place recognition using 3D LiDAR scans, enabling accurate retrieval and orientation estimation in outdoor environments, outperforming existing approaches.

## Contribution

The paper presents a novel CNN-based descriptor for 3D LiDAR scans, trained with triplet loss and hard-negative mining, specifically designed for outdoor place recognition and orientation estimation.

## Key findings

- Outperforms state-of-the-art methods on NCLT and KITTI datasets.
- Effective in challenging long-term outdoor scenarios.
- Provides both place retrieval and yaw discrepancy estimation.

## Abstract

We introduce a novel method for oriented place recognition with 3D LiDAR scans. A Convolutional Neural Network is trained to extract compact descriptors from single 3D LiDAR scans. These can be used both to retrieve near-by place candidates from a map, and to estimate the yaw discrepancy needed for bootstrapping local registration methods. We employ a triplet loss function for training and use a hard-negative mining strategy to further increase the performance of our descriptor extractor. In an evaluation on the NCLT and KITTI datasets, we demonstrate that our method outperforms related state-of-the-art approaches based on both data-driven and handcrafted data representation in challenging long-term outdoor conditions.